Identification and the potential involvement of miRNAs in the regulation of artemisinin biosynthesis in A. annua - Nature
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
www.nature.com/scientificreports
OPEN Identification and the potential
involvement of miRNAs
in the regulation of artemisinin
biosynthesis in A. annua
Shazia Khan, Athar Ali, Monica Saifi, Parul Saxena, Seema Ahlawat & Malik Zainul Abdin*
Micro RNAs (miRNAs) play crucial regulatory roles in multiple biological processes. Recently they
have garnered the attention for their strong influence on the secondary metabolite production in
plants. Their role in the regulation of artemisinin (ART) biosynthesis is, however, not fully elucidated.
ART is a potent anti-malarial compound recommended by WHO for the treatment of drug-resistant
malaria. It is produced by Artemisia annua (A. annua). The lower in planta content of ART necessitates
a deep understanding of regulatory mechanisms involved in the biosynthesis of this metabolite. In
this study, using modern high throughput small RNA-sequencing by Illumina Nextseq 500 platform
for identification and stem-loop RT PCR for validation, miRNAs were identified in the leaf sample of A.
annua plant. Here, we report a total of 121 miRNAs from A. annua that target several important genes
and transcription factors involved in the biosynthesis of ART. This study revealed the presence of some
important conserved miRNA families, miR396, miR319, miR399, miR858, miR5083 and miR6111
not identified so far in A. annua. The expression patterns and correlation between miRNAs and their
corresponding targets at different developmental stages of the plant using real-time PCR indicate
that they may influence ART accumulation. These findings thus, open new possibilities for the rational
engineering of the secondary metabolite pathways in general and ART biosynthesis in particular.
Abbreviations
ART Artemisinin
A. annua Artemisia annua
miRNA MicroRNA
TF Transcription factor
Artemisia annua (A. annua), an important medicinal herb is the only natural source of artemisinin (ART). ART,
a sesquiterpene lactone is the most reliable and widely used drug to quell Malaria, which is one of the most
devastating diseases and a leading cause of deaths worldwide. According to WHO, there were a staggering 228
million cases and 405,000 deaths reported in 2018 alone, despite the disease being preventable as well as c urable1.
WHO has recommended the use of ART only in combination of other drugs, to slow down the development
of resistance in Plasmodium sps. against it and to maintain its efficacy. The combination treatment is widely
known as artemisinin-based combination therapies (ACTs). In wild type A. annua, ART is present in the aerial
parts, in extremely low quantities (0.02–1.07%)2. Numerous efforts were made to generate ART synthetically
or semi-synthetically, but does not meet industrial practices due to high production cost. The insufficient sup-
ply of ART produced by plants or synthetic methods, resulted in the lack of ACTs available to the patients and
causes approximately half a million mortalities every y ear3. Despite various efforts to produce semi-synthetic
ART at low cost, the natural source of ART remains the main provider. Thus, researchers are investigating effec-
tive strategies to enhance in planta content of ART. The Ian Grahm lab and Bill and Malinda gates foundation
invested huge fund in production of high ART yielding hybrids and succeeded in development of HYB8001R that
could produce 1.4% ART of dry weight4. Reconstitution of the pathway and increasing the area under cultiva-
tion could be a promising solution to increase ART production. Ding et al., 2020 suggested the potential fields
Department of Biotechnology, School of Chemical and Life Sciences, Centre for Transgenic Plant Development,
Jamia Hamdard, New Delhi 110062, India. *email: mzabdin@jamiahamdard.ac.in
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 1
Vol.:(0123456789)
www.nature.com/scientificreports/
Figure 1. Length distribution graph of small RNA sequences in leaf sample library of A. annua.
suited best for the cultivation of A. annua plant worldwide. They found that contrary to the higher demands in
African region the potential fields are minimal. The best suited land resources were predicted to be distributed
around the south eastern coast of Brazil and A ustralia5. Thus, the strong international cooperation is needed to
resolve this spatial mismatch.
The reconstitution of the pathway, requires the complete elucidation of the biosynthetic pathway genes and
regulatory elements. In plants, the secondary metabolites biosynthesis and accumulation is highly regulated.
ART biosynthesis has been extensively studied and it was found that two metabolic pathways; cytoplasmic
mevalonic acid (MVA) and plastidial methylerythritol phosphate (MEP) are involved in providing the basic
carbon precursors of isoprene units for the biosynthesis of ART. To date, genes encoding enzymes of MVA and
MEP pathways such as HMGR, IDI, DXS, DXR, HDR and FPS as well as genes for all five downstream enzymes
of ART biosynthesis pathway namely ADS, CYP71AV1/CPR, ADH1, ALDH1, and DBR2 are over-expressed in
A. annua plants6–13. Shen et al. (2018) showed up to 3.2% enhancement in the production of ART in A. annua
transgenic lines that were simultaneously overexpressing the FPS, HMGR and DBR2 g enes14. Role of TF in the
regulation of complex pathways is well known and cannot be neglected, when a lot of reports are advocating their
regulatory role in secondary metabolite biosynthesis. Consequently, TFs were cloned and their effect on ART
content was studied. It was shown that AaMYB1 significantly enhance the transcription of key genes of the ART
biosynthetic pathway, especially ADS and CYP71AV1, that resulted in an increased ART content in transgenic
plants15. Similar results were obtained when other TFs such as AaERF1, AabZIP, bHLH, AaWRKY, AaMYC2,
AaGSW1, AaORA and AaTAR1 were overexpressed16–19. Due to the fine tuning of pathways and probably due to
the presence of other key players, these efforts were however, unable to effectively boost up the entire metabolic
flux towards ART biosynthesis. In a study, it was however, shown that the overexpression or loss of miR163 alters
entire secondary metabolite profile in mir163 mutant of Arabidopsis20. Also, another study revealed that miR156-
trageted SPL9 directly binds terpene synthase promoter and regulate β-caryophyllene synthesis in Arabidopsis21.
Thus, it seems reasonable to assume that miRNAs can also be used as effective regulators for modulating ART
biosynthesis based on their targets in ART biosynthetic pathway. Here, we are reporting, miRNAs from A. annua
and their possible involvement in ART biosynthesis.
Results
Sequencing of small RNAs and data analysis. A high-quality RNA (RIN number 8.4), isolated from
the leaves of A. annua at pre-flowering stage was subjected to next generation sequencing (NGS), thereby
resulted in generation of 36,392,552 raw reads. As a result of further analysis, i.e. selection of size, trimming of
adapters and low-quality reads, 34,741,778 clean reads (unique reads 5,228,750) were remained. Further remov-
ing the other non-coding RNA sequences such as snRNA, siRNA, tRNA, rRNA, snoRNAs resulted in 21,835,603
clean reads (unique reads 4,199,521). Among the clean reads the maximum reads were of 24nt followed by 21nt
and then 20, 22 and 23 nt reads. The length distribution graph is given in Fig. 1. The 23 and 24nt sequences pos-
sess 5′-adenosine at the first position, generally considered as endogenous siRNAs 22. These are one of the most
abundant classes of small RNAs in plants, while most plant miRNAs tend to be 21 and 22nt in length 23.
Identification of A. annua conserved and novel miRNAs. The clean reads obtained were subse-
quently analysed to predict conserved and novel miRNAs in A. annua. As a result, 121 miRNAs were identified,
80 of which were conserved across plant species whereas, 41 candidates were predicted as A. annua specific novel
miRNAs (supplementary file S1). The conserved miRNAs belong to 23 different families of miRNAs. Varying
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 2
Vol:.(1234567890)www.nature.com/scientificreports/
S. No MicroRNA Abundance (reads per million)
1 miR396f 261,298
2 miR159a 73,683
3 miR166a-3p 47,883
4 miR166m 19,512
5 miR166n 19,240
6 miR396g-5p 11,558
7 miR396b-5p 8,444
8 miR166g-3p 6,714
9 miR162a-3p 5,741
10 miR159a.1 4,930
Table 1. Ten most abundant conserved miRNA families in A. annua leaf sample library.
S. No. miRNA family No. of members
1 miR166 16
2 miR396 12
3 miR159 9
4 miR162 5
5 miR156 4
6 miR167 4
7 miR319 4
8 miR398 4
9 miR160 2
10 miR164 2
11 miR165 2
12 miR168 2
13 miR172 2
14 miR399 2
15 miR403 2
16 miR170 1
17 miR171 1
18 miR393 1
19 miR394 1
20 miR858 1
21 miR5083 1
22 miR5368 1
23 miR6111 1
Table 2. No. of members in conserved miRNA families in A.annua.
level of miRNA expression was found in the library. Out of all, the maximum expression was observed for miR-
NA396f, followed by miRNA159a with 261,298 and 73,683 reads, respectively. The ten most abundant miRNAs
in the sample are shown in Table 1.
A total of 248 sequences of 18–22 nt were also identified that were either 1-2nt shorter or longer than miRNA
in miRbase-21 (supplementary file S1). In our data, the highest number of miRNAs belongs to miR166 and
miR396 families having 16 members and 12 members respectively. While, families miR170, miR171, miR393,
miR394, miR858, miR5083, miR5368, and miR6111 were found to have only one member. Number of members
in each family are summarized in Table 2.
Furthermore, a total of 41 sequences were successfully identified as novel miRNA candidates in A. annua.
These sequences have a length ranging from 18 to 24 bp, but the maximum among them were 20–22 bp long.
The minimum fold energy (MFE) observed was between − 18.3 kcal-mol−1 to − 84.4 kcal-mol−1. This showed
good agreement with previous reports on novel miRNA predictions in other species (MFE of Catharanthus
roseus, − 43.2 kcal-mol−1; Opium poppy (Papaver somniferum), − 34.09 kcal-mol−1; pigeonpea (Cajanus cajan),
− 34.8 kcal-mol1; Arabidopsis thaliana, rice (Oryza sativa), soybean (Glycine max), Medicago truncatula, Sac-
charum officinarum, sorghum (Sorghum bicolor) and maize (Zea mays) from − 40 to 100 kcal-mol−1)24–27. The
minimum fold energy index (MFEI) is an important criterion to characterize miRNAs. When the MFEI is more
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 3
Vol.:(0123456789)www.nature.com/scientificreports/
Figure 2. Agarose gel based detection of miRNAs (A) conserved miRNAs M:50 bp ladder, 1: miR5083, 2:
miR858b, 3: miR159a, 4: miR396, 5: miR172a-3p, 6: miR166i, 7: miR166g, 8: miR162 (B) novel miRNAs M:
50 bp ladder, 1:miRn-7, 2: miRn-10, 3: miRn-11, 4: miRn-18, 5:miRn-29, 6: miRn-26.
than 0.85, the sequence is most likely to be miRNA. It was observed that the MFEIs of stem loop structures of
the miRNAs were ranged from 0.87 to 2.1. All the information along with precursor sequences is given in sup-
plementary file S1.
Validation of conserved and novel miRNAs. The miRNAs obtained by NGS were validated using stem-
loop PCR according to published protocol28. Of the 15 selected miRNAs (8 conserved and 7 novel miRNAs), we
confirmed the expression of 14 miRNAs (Fig. 2).
To further strengthen our results, all these miRNAs were also checked at different developmental stages of the
plant. All these miRNAs were successfully amplified in all the stages. Thus, the results obtained in this experi-
ment and the data obtained by NGS present enough support for the existence of these miRNAs in A. annua.
Target prediction. As a result of target prediction, 54 unique targets of 52 conserved miRNAs, and more
than 60 targets of 30 novel miRNAs were successfully identified. Results are given in supplementary file S1. Target
interpretation was mainly focused on their putative roles in ART biosynthesis. Multiple miRNAs were found to
target single mRNA. For instance, three families of miRNAs, namely miR159, miR166, miR172, and four novel
miRNAs namely miRn-8, miRn-20, miRn-36 and miRn-37 putatively targets cytochrome p450 reductase (CPR)
mRNA. CPR is a redox partner of CYP71AV1, an important enzyme in ART biosynthesis. Another important
miRNA family is miR396 family. The three members of this family, viz. miR396, miR396f and miR396g-5p
targets DXR1, a rate limiting enzyme of plastidial MEP pathway. Similarly, other enzymes of MVA and MEP
pathways such as HMGR, IDI, DXS, HDR and FPP are targeted by several miRNAs. The details are given in
supplementary file S1. These enzymes play an important role in producing basic isoprene units for production
of all terpenoids. It was observed that miR858 putatively target transcript for the enzyme ADS. Other significant
miRNAs were miRn-11, miRn-17, miRn-18, that targets CYP71AV1 mRNA. Both of the enzymes are key play-
ers in ART biosynthesis and thus, these miRNAs could play crucial roles in the ART biosynthesis regulation. In
addition to above mentioned miRNAs, we have identified miRNAs that targets TFs. We observed that miR858
targets transcription factor family MYB. In A. annua, MYB1 is involved in ART biosynthesis regulation15. It
was found that in Arabidopsis, miRNA858 targets several members of MYB family that regulate the synthesis
of flavonoids29. Consistent with these reports, miR858 in A. annua targets various members of MYB. In addi-
tion, miR858 was also predicted to target ADS transcript. ADS is one of the key enzymes in ART biosynthesis
regulation. Another important TF, NAC1 is a putative target of miRNA families 403 and 5,083. In literature,
NAC1 is reported to regulate two key genes ADS and CYP71AV1 of ART biosynthesis pathway. WRKY1 the
third TF putatively targeted by miR166g is also reported to drag ART biosynthesis pathway in positive direction
and help in accumulation of ART by regulating pathway genes. Similarly, MYC2 is the putative target of miRn-
26 and miRn-41. Apart from targeting transcripts of gene, few miRNAs targets promoter region of the genes.
This includes miR159, miR172a-3p, miR166i, miRn-7, miRn-10, miRn-24, and miRn-29. Of which miR166i
and miRn-7 target DBR2 gene promoter; miR159, miR172a-3p and miRn-10 target ERF1 gene promoter while
miRn-24 and miRn-29 target promoters of WRKY1 and ADS genes, respectively.
Additionally, we have found that the mRNAs of β-amyrin synthase, β-farnesene synthase, and linalool syn-
thase, which are involved in the formation of other terpene molecules are targeted by some conserved and A.
annua specific novel miRNAs listed in supplementary file S1. Many researchers showed that blocking the path-
ways involved in biosynthesis of other terpene molecules, redirect the carbon flux towards ART biosynthesis in A.
annua. Some important miRNAs involve in ART biosynthesis were given in Fig. 3. The detailed target prediction
data is available in supplementary file S1.
Differential expression and correlation of miRNAs and their putative targets. The expressions
of six miRNAs, viz. miR858, miR5083, miR159, miRn-7, miRn-10, miRn-29 and their putative target genes
evaluated at different developmental stages viz., vegetative, pre-flowering and flowering stages of the plant using
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 4
Vol:.(1234567890)www.nature.com/scientificreports/
Figure 3. ART biosynthesis and putative involvement of miRNAs. The MVA pathway and MEP pathway
products are shown in orange boxes. HMG-CoA hydroxymethylglutaryl CoA, IPP isopentenyl diphosphate,
DMAPP dimethylallyl diphosphate, FPP farnesyl diphosphate, DXP 1-deoxy-d-xylulose 5-phosphate, MEP
methyl erythritol phosphate, enzymes are indicated in grey boxes, HMGS HMG-CoA synthase, HMGR HMG-
CoA reductase, IDI IPP isomerase, DXS 1-deoxy-d-xylulose 5-phosphate synthase, DXR DXP reductoisomerase,
ADS armorpha-4, 11-diene synthase, CYP71AV1 cytochrome P450 mono-oxygenase, CPR cytochrome P450
reductase, DBR2 artemisinic aldehyde delta-11(13)-double bond reductase, ALDH1 aldehyde dehydrogenase,
RED1 dihydroartemisinic aldehyde reductase, transcription factors are indicated in green boxes and miRNAs
are indicated in blue circles.
qRT-PCR are shown in Figs. 4 and 5. Results showed that miR858 was negatively correlated with its targets
MYB1 and ADS (Pearson coefficient − 0.52, − 0.50). Similarly, miR159 was negatively correlated with its target
CPR (Pearson coefficient − 0.68). Interestingly, the expression of miR5083 was positively correlated with its
target NAC1 (Pearson coefficient + 0.4). Of the four miRNAs viz. miR159, miRn-7, miRn-10, and miRn-29 that
target promoter regions of ERF1, DBR2 and ADS genes, the expression levels of miRn-7 had a positive correla-
tion with its target gene DBR2 expression (Pearson coefficient + 0.39). The expressions of miRn-10, miR159 and
miRn-29 were not found to be correlated with the expression of their target genes.
Discussion
Micro RNAs are small non coding RNAs that exert extensive impact on the regulation of gene expression in
both plants and animals. In recent years, they have garnered attention due to their involvement in a number of
complex biological processes including the secondary metabolite production in plants30. Their possible involve-
ment in ART biosynthesis is however, yet to be elucidated. Previous three reports on miRNAs identification
and target prediction in A. annua were based on computational identification and predicted only six, thirteen
and eleven miRNAs, respectively that may represent only a fraction of its miRNAome31–33. Here, we sought to
identify and validate miRNAs present in A. annua plant using NGS along with qPCR. Modern deep sequenc-
ing/NGS has many advantages over computational prediction specially in species that do not have full genome
sequences. A total of 121 miRNAs were identified that include some important conserved miRNAs like miR396,
miR319, miR399, miR858, miR5083 and miR6111, which were not identified so far in A. annua. It was recently
shown that miR319 regulate TCP4 proteins, involved in suppression of trichome branching in Arabidopsis thali-
ana34. Trichomes are factories for synthesis and accumulation of most secondary metabolites including ART35,36.
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 5
Vol.:(0123456789)www.nature.com/scientificreports/
Figure 4. Relative expression of selected miRNAs levels quantified by real-time PCR at vegetative, pre-
flowering and flowering stages of the A. annua plant. The expression levels of miRNAs were normalized to the
expression level of U6 RNA. Relative expression was calculated using 2 ^− ΔCt equation, where Ct = (CtmiRNA − Ct
U6). ANOVA followed by Dunnett’s test was applied to all miRNAs compared to U6. The error bar shows the
standard error. *P < 0.05, ***P < 0.001.
Besides, reports on miR858 family mediated regulation of secondary metabolites are accumulating in model as
well as non-model p lants37,38. Thus, identification of these new classes of miRNAs may provide new directions
to unveil the regulatory mechanisms controlling secondary metabolites biosynthesis and accumulation, and for
rational engineering of ART biosynthetic pathway. NGS also allowed us to predict 41 novel miRNAs, based on
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 6
Vol:.(1234567890)www.nature.com/scientificreports/
Figure 5. Relative expression of selected mRNAs levels at vegetative, pre-flowering and flowering stages of the
A. annua plant. The expression levels of mRNAs were normalized to the expression level of β-actin. Relative
expression was calculated using 2 ^− ΔCt equation, where Ct = (Ctgene − Ct β-actin). ANOVA followed by Dunnett’s
test was applied to all mRNAs compared to β-actin. The error bar shows the standard error. *P < 0.05, **P < 0.01,
***P < 0.001.
their hairpin structures. Expression of A. annua specific novel miRNAs were lower as compared to conserved
miRNAs, which is often the case of species-specific m iRNAs22,39. Validation of fifteen miRNAs by stem-loop
RT-PCR at different developmental stages of the plant further strengthen our NGS data.
Interestingly, some of the miRNAs target multiple genes, for instance, miR396e had eight targets while others
like miR159a, miR159b, miR159c and miR159f were found to target the mRNA of the same gene. These results
exhibit the complex miRNA-target network in A. annua.
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 7
Vol.:(0123456789)www.nature.com/scientificreports/
This study was focused on identification of miRNAs involved in the regulation of ART biosynthesis. Results
showed that three families of conserved miRNAs, viz. miR159, miR172 and miR166 were targeting a cytochrome
P450 reductase (CPR) gene. CPR, along with CYP71AV1 catalyzes three steps of ART biosynthesis, oxidizing
amorpha 4,11 diene to artemisinic acid40,41. qPCR data suggested that higher expression of miR159 is negatively
correlated with target CPR gene expression (Pearson coefficient -0.68) might play a role in the repression of CPR
gene at the pre-flowering stage. Another significant miRNA, miR858 targets ADS mRNA. ADS is an important
enzyme in ART biosynthesis regulation. Expression of miR858 at different developmental stages is negatively
correlated with ADS gene expression (Pearson coefficient − 0.50). miR858 was also targeted MYB TF as discussed
later in this section. Besides, we also identified the nine A. annua specific miRNAs targeting important genes
in ART biosynthesis.
Some of the TFs and miRNAs, while independently regulating their targets, collaborate with each other to
regulate gene expression. Literature showed the involvement of at least seven families of TFs in ART biosynthesis
that include AP2/ERF, bZip, bHLH, NAC, WRKY, MYC and MYB families15,16,42,43. In our results, it was found
that miR858 targets several MYB TFs. The qPCR results revealed that the miR858 accumulation is negatively
correlated with that of MYB1 (Pearson coefficient − 0.52), when checked at different developmental stages of
the plant. Hernández et al.15 predicted the binding sites of R2R3 type of AaMYB1 in ADS, CYP71AV1, DBR2
promoters. They showed that the content of ART doubles in transgenic lines when AaMYB1 was cloned and
overexpressed. Evidences on regulation of MYBs by miR858 are accumulating in plants such as Arabidopsis,
Actinidia arguta (kiwifruit), Vitis vinifera (grapes), and Ipomoea batatas (sweetpotato), where they play key roles
in the synthesis of secondary metabolites like anthocyanin and flavonoids37,38,44,45. It was also found that miR858
is involved in fungal susceptibility, and inhibiting miR858 by miRNA mimic technique trigger fungal resistance
in Arbidopsis thaliana46. We speculate that miR858 may play similar roles in A. annua. NAC1, another important
TF involved in ART biosynthesis was found to be the target of two families of conserved miRNAs, miR403 and
miR5083. Most of the plant miRNAs exert negative regulation on their target genes. There are findings where
their expressions are however, positively correlated. For example, Kawashima et al.47 revealed that the expres-
sions of miR395 and its target gene, sulphate transporter (SULTR2;1) were positively correlated in the roots of
Arabidopsis plants. Thus, it was interesting to discover a positive correlation in the expression of miR5083 with
its target NAC1 gene (Pearson coefficient + 0.4). Similar kind of correlations were also reported in other living
systems, suggesting that this is not restricted only to plants48. miR166g targets AaWRKY1 that regulates ART
biosynthesis by binding to the W box in the ADS promoter49. It was shown that transgenic lines overexpressing
AaWRKY1 lead to accumulate 1.9-fold higher ART c ontent50. Similarly, TF AaERF1 was the putative target of
miR159, miR172a-3p, miRn-10, miRn-20, and miRn-39, while two miRNAs, miRn-26 and miRn-41 could bind
MYC2. We speculate that it will be more rational to modulate the TFs that regulate the multiple genes of ART
pathway, by targeting miRNAs than to overexpress individual gene to enhance ART content.
Target prediction has led us to identify few miRNAs that target promoter regions of the important genes
involved in ART biosynthesis. One of such miRNAs is miRn-7 that targets DBR2 gene promoter region. Induction
of DBR2 gene leads to higher accumulation of ART10. Similarly, miRNA159, miR172a-3p, miR166i, miRn-24 and
miRn-29 target the promoter regions of ERF1, DBR2 WRKY1, and ADS genes. It was observed that miRNAs
that target the promoters of genes, activate their expression at the transcriptional level. For instance, miR5658
directly activates AT3G25290 expression by targeting its promoter in Arabidopsis thaliana.51. Similarly, Place
et al.52 reported that miR-373 induces the expression of two genes, cold-shock domain-containing protein C2
(CSDC2) and E-cadherin by binding the promoters in PC-3 prostate cancer cells52. We observed that the expres-
sion of miRn-7 is positively correlated with the expression of its target gene DBR2 (Pearson coefficient + 0.39)
at different developmental stages of A. annua plant. We presume that it could play important regulatory role in
ART biosynthesis. The expression of miRn-29, miRn-10 and miR159 were not correlated with the expression of
their target genes (Pearson coefficient < 0.3).
Apart from miRNAs targeting ART biosynthesis, it was interesting to discover microRNAs that target genes
involved in biosynthetic pathways of other secondary metabolites. These pathways interact with ART pathway
while competing for the common precursors generated by MVA pathway. We found that one conserved miRNA,
miR162 was putatively targeting chalcone synthase mRNA while five novel miRNAs namely, miRn-1, miRn-4,
miRn-18, miRn-20, miRn-26 were targeting chrysanthemyl synthase, cinnamyl alcohol dehydrogenase, β-amyrin
synthase, linalool synthase and β-farnesene synthase mRNAs, respectively. Researchers showed that by inhibiting
the pathways competing for the common precursors lead to divert the carbon flux towards ART biosynthesis and
resulted in accumulation of higher ART in transgenic A. annua plants53,54. Further studies would however, be
required for assessing the impact of these miRNAs on secondary metabolite profile. Thus, this study provides a
new insight into the possible miRNA-directed molecular processes that regulate ART biosynthesis in A. annua.
Methods
To identify miRNAs in the wild type A. annua, ‘cim-arogya’ variety was grown in the herbal garden of Jamia
Hamdard, New Delhi, India. The seeds of these plants were obtained from Ipca Laboratories Pvt Ltd, M.P., India.
It was a high artemisinin producing variety and contains 0.9 to 1.1% of artemisinin content55. Plant leaf samples
at pre flowering stage were collected for RNA isolation.
RNA extraction. For RNA extraction, leaf samples were grounded with mortar and pestle in liquid nitro-
gen. RNA was isolated using TRIzol reagent (Invitrogen, USA) as per manufacturer’s instructions. Total RNA
integrity was checked on 1% formaldehyde gel and concentration of RNA was determined using nanodrop
electrophotometer (Thermo Fischer Scientific, USA) while the RNA quality was checked on the BioAnalyzer
(Agilent, USA).
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 8
Vol:.(1234567890)www.nature.com/scientificreports/
Library preparation and sequencing. RNA sample were sent for high throughput sequencing to M/S
Sandor Lifesciences Pvt Ltd, Hyderabad, India. Small RNA library was generated using Illumina NextSeq 500
platform. NEB adaptor having sequence ‘AGATCGGAAGAGCACACGTCT’ was added for sequencing. Redun-
dant reads were counted and reads per million (RPM) values were calculated. Low quality reads were removed
before processing data. Raw reads were then processed, and adapter sequences were trimmed using Cutadapt
tool. Remaining reads were then searched against Rfam database to eliminate other non-coding RNAs like
rRNA, tRNA, snRNA and snoRNAs.
Identification of A. annua miRNAs. The clean reads thus obtained were used to predict conserved and
novel miRNAs. For conserved miRNA prediction clean reads were aligned to miRBase-21 (https://www.mirba
se.org/) allowing up to 2 mismatches. The unaligned sequences left were used to predict novel miRNAs. At the
time of sequencing the sRNAs, the genome of A. annua was not available. Although, it has been assembled
in 2018, but the unavailability of complete annotation limits its application as good reference genome. Thus,
for novel miRNA prediction, unique sRNA sequences were mapped to the EST sequences of A. annua and
transcriptome of A. annua submitted to the National Center for Biotechnology Information (NCBI) database
(https://www.ncbi.nlm.nih.gov/). Mature as well as stem-loop structures were predicted for novel miRNAs using
mireap_0.2 software. The criteria used were as follows: (i) The putative mature miRNAs were required to have
length between 18 and 24 bases and at least have reads per million (RPM) > 2; (ii) There were no more than five
mismatches and four bulges between mature miRNA candidate and the opposite hairpin arm; and (iii) the upper
limit of minimum fold energy (MFE) of miRNAs was set to − 18 kcal-mol−1. Hairpin loops of all novel miRNAs
were constructed by mfold web server using RNA folding form.
Target search of A. annua miRNAs. The functional annotation of miRNAs was obtained using
psRNATarget56, one of the most reliable and widely used tool to predict the targets gene of the miRNAs in plants
with default parameters. A. annua nucleotide sequences from NCBI database (https://www.ncbi.nlm.nih.gov/
unigene) were taken as data set for predicting the targets.
Stem‑loop RT‑PCR. Results obtained from the next generation sequencing (NGS) were validated using
stemloop RT-PCR, one of the most commonly used and reliable method for determination of miRNAs. Fifteen
miRNAs were selected for validation by stemloop RT-PCR according to the published p rotocol28. The basis of
selection of miRNAs was their targets in the artemisinin biosynthesis pathway. Amplification was obtained using
standard PCR. Stemloop RT primers were designed for each of the fifteen miRNAs for reverse transcription and
cDNA was prepared using RevertAid cDNA synthesis kit (Thermo Fischer scientific, USA). Reverse transcrip-
tion reaction mixture was kept in thermal cycler (Thermo fischer scientific) under following condition: 1 cycle
of 16 °C for 30 min; 60 cycles of 30 °C for 30 s, 42 °C for 30 s, 50 °C for 1 s, and 1 cycle of 70 °C for 15 min as
described by Gasic et al. 28,57.
Relative expression profiling of miRNAs and their putative target genes in ART biosynthesis
pathway. To analyse the possible association of miRNAs and their putative target genes in ART biosynthesis
pathway, expression of six selected miRNAs as well as their target genes were analysed by RT-qPCR. The basis
of selection of miRNAs was their putative targets in ART biosynthesis pathway as well as their specificity. The
miRNAs having only one or two targets were selected for expression analysis.
The leaf tissues from different developmental stages were taken. The stages of growth were considered from the
days after transplanting (DAT). Leaves were collected at vegetative (90 days from DAT), pre-flowering (180 days
from DAT-budding stage), and flowering stages (200 days from DAT) for RNA isolation. Three biological rep-
licates were taken from each stage. Primers for miRNAs were designed as described by Gassic et al., and the
primers for the genes were designed using IDT Primer quest tool. Primer sequences used in the study are given
in supplementary file S1. Total RNA was isolated using TRIzol reagent (Invitrogen, USA). 1 µg of high-quality
RNA (RIN 0.8–0.95) was taken for reverse transcription reactions in 20 µl reaction mixture using RevertAid
cDNA synthesis kit (Thermo Scientific, USA).
After RT reaction cDNAs were diluted 10 times. The reactions for qPCR were performed in Roche light cycler
480 (Roche) with SYBR Green PCR Master Mix (Roche) in 10 µl tubes on 96 well plates. All reactions were rep-
licated three times. To normalize the data U6 and β-actin were taken as internal controls for miRNAs and genes
respectively. To finally calculate the relative expression, 2^− ΔCt equation was used, where Ct = (CtmiRNA − CtU6)
for miRNAs and Ct = (Ctgene − Ctβ-actin) for genes. The specificity of amplification was checked using melt curve
analysis; no primer dimers were observed.
Statistical analysis. The significant differences of miRNAs and their target genes with their reference
genes at different developmental stages of the plant were calculated. Analysis was performed using one-way
ANOVA followed by Dunnett’s post hoc test. The data are presented as means ± S.E. The correlation between
each miRNA and its target mRNA was investigated using the Pearson correlation test. A perfect negative corre-
lation was defined as r = − 1 and a perfect positive correlation as r = + 1. Correlation values between -0.3 to + 0.3
were taken as no correlation between miRNA and their t argets58. Results were considered statistically significant,
when p < 0.05. All statistical analysis were performed using the GraphPad Prism 5.00,GraphPad Software, Inc.,
La Jolla, CA, USA59.
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 9
Vol.:(0123456789)www.nature.com/scientificreports/
Received: 21 February 2020; Accepted: 17 June 2020
References
1. Organization, W. H. World Malaria Report 2019. (2019).
2. Organization, W. H. The Use of Non-Pharmaceutical Forms of Artemisia. (World Health Organization, 2019).
3. Xie, D.-Y., Ma, D.-M., Judd, R. & Jones, A. L. J. P. R. Artemisinin biosynthesis in Artemisia annua and metabolic engineering:
Questions, challenges, and perspectives. 15, 1093–1114 (2016).
4. Suberu, J., Gromski, P. S., Nordon, A., Lapkin, A. J. J. O. P. & Analysis, B. Multivariate data analysis and metabolic profiling of
artemisinin and related compounds in high yielding varieties of Artemisia annua field-grown in Madagascar. 117, 522–531 (2016).
5. Ding, F. et al. Mapping worldwide environmental suitability for Artemisia annua L. Sustainability 12, 1309 (2020).
6. Teoh, K. H., Polichuk, D. R., Reed, D. W. & Covello, P. S. Molecular cloning of an aldehyde dehydrogenase implicated in artemisinin
biosynthesis in Artemisia annua. Botany 87, 635–642. https://doi.org/10.1139/B09-032 (2009).
7. Tang, K., Shen, Q., Yan, T. & Fu, X. Transgenic approach to increase artemisinin content in Artemisia annua L. Plant Cell Rep. 33,
605–615. https://doi.org/10.1007/s00299-014-1566-y (2014).
8. Han, J. L. et al. Effects of overexpression of the endogenous farnesyl diphosphate synthase on the artemisinin content in Artemisia
annua L. J. Integr. Plant Biol. 48, 482–487. https://doi.org/10.1111/j.1744-7909.2006.00208.x (2006).
9. Alam, P. & Abdin, M. Over-expression of HMG-CoA reductase and amorpha-4, 11-diene synthase genes in Artemisia annua L.
and its influence on artemisinin content. Plant Cell Rep. 30, 1919–1928. https://doi.org/10.1007/s00299-011-1099-6 (2011).
10. Yuan, Y. et al. Overexpression of artemisinic aldehyde Δ11 (13) reductase gene–enhanced artemisinin and its relative metabolite
biosynthesis in transgenic Artemisia annua L. Biotechnol. Appl. Biochem. 62, 17–23 (2015).
11. Liu, M. et al. Characterization of a trichome-specific promoter of the aldehyde dehydrogenase 1 (ALDH1) gene in Artemisia annua.
Plant Cell Tissue Organ Cult. 126, 469–480. https://doi.org/10.1007/s11240-016-1015-4 (2016).
12. Ma, D. et al. Overexpression of a type-I isopentenyl pyrophosphate isomerase of Artemisia annua in the cytosol leads to high
arteannuin B production and artemisinin increase. Plant J. 91, 466–479 (2017).
13. 13Ma, D., Li, G., Zhu, Y. & Xie, D.-Y. J. F. I. P. S. Overexpression and suppression of Artemisia annua 4-hydroxy-3-methylbut-2-
enyl diphosphate reductase 1 gene (AaHDR1) differentially regulate artemisinin and terpenoid biosynthesis. 8, 77 (2017).
14. Shen, Q. et al. The genome of Artemisia annua provides insight into the evolution of Asteraceae family and artemisinin biosynthesis.
Mol. Plant 11, 776–788. https://doi.org/10.1016/j.molp.2018.03.015 (2018).
15. Matias-Hernandez, L. et al. AaMYB1 and its orthologue AtMYB61 affect terpene metabolism and trichome development in
Artemisia annua and Arabidopsis thaliana. Plant J. 90, 520–534. https://doi.org/10.1111/tpj.13509 (2017).
16. Shen, Q., Yan, T., Fu, X. & Tang, K. Transcriptional regulation of artemisinin biosynthesis in Artemisia annua L. Sci. Bull. 61, 18–25.
https://doi.org/10.1007/s11434-015-0983-9 (2016).
17. Yu, Z.-X. et al. The jasmonate-responsive AP2/ERF transcription factors AaERF1 and AaERF2 positively regulate artemisinin
biosynthesis in Artemisia annua L. Mol. Plant 5, 353–365. https://doi.org/10.1093/mp/ssr087 (2012).
18. Chen, M. et al. GLANDULAR TRICHOME‐SPECIFIC WRKY 1 promotes artemisinin biosynthesis in Artemisia annua. 214,
304–316 (2017).
19. Lu, X. et al. A a ORA, a trichome‐specific AP 2/ERF transcription factor of Artemisia annua, is a positive regulator in the arte-
misinin biosynthetic pathway and in disease resistance to Botrytis cinerea. 198, 1191–1202 (2013).
20. Ng, D. W. et al. cis- and trans-Regulation of miR163 and target genes confers natural variation of secondary metabolites in two
Arabidopsis species and their allopolyploids. Plant Cell 23, 1729–1740. https://doi.org/10.1105/tpc.111.083915 (2011).
21. Yu, Z. X. et al. Progressive regulation of sesquiterpene biosynthesis in Arabidopsis and Patchouli (Pogostemon cablin) by the
miR156-targeted SPL transcription factors. Mol. Plant 8, 98–110. https://doi.org/10.1016/j.molp.2014.11.002 (2015).
22. Axtell, M. J. Classification and comparison of small RNAs from plants. Annu. Rev. Plant Biol. 64, 137–159. https: //doi.org/10.1146/
annurev-arplant-050312-120043 (2013).
23. Axtell, M. J. & Meyers, B. C. Revisiting criteria for plant microRNA annotation in the era of big data. Plant Cell 30, 272–284. https
://doi.org/10.1105/tpc.17.00851(2018).
24. Zhang, B. H., Pan, X. P., Cox, S. B., Cobb, G. P. & Anderson, T. A. Evidence that miRNAs are different from other RNAs. Cell Mol
Life Sci 63, 246–254. https://doi.org/10.1007/s00018-005-5467-7 (2006).
25. Shanmugavadivel, P., Soren, K., Konda, A. K., Chaturvedi, S. & Singh, N. Identification of potential stress responsive microRNAs
and their targets in Cajanus spp. Agri Gene 1, 33–37. https://doi.org/10.1016/j.aggene.2016.06.001 (2016).
26. Boke, H. et al. Regulation of the alkaloid biosynthesis by mi RNA in opium poppy. Plant Biotechnol. J. 13, 409–420. https://doi.
org/10.1111/pbi.12346(2015).
27. Shen, E. M. et al. The miRNAome of Catharanthus roseus: Identification, expression analysis, and potential roles of microRNAs
in regulation of terpenoid indole alkaloid biosynthesis. Sci. Rep. 7, 43027. https://doi.org/10.1038/srep43027 (2017).
28. Varkonyi-Gasic, E., Wu, R., Wood, M., Walton, E. F. & Hellens, R. P. Protocol: a highly sensitive RT-PCR method for detection
and quantification of microRNAs. Plant Methods 3, 12. https://doi.org/10.1186/1746-4811-3-12 (2007).
29. Dubos, C. et al. MYB transcription factors in Arabidopsis. Trends Plant Sci. 15, 573–581. https://doi.org/10.1016/j.tplan
ts.2010.06.005 (2010).
30. Gupta, O. P., Karkute, S. G., Banerjee, S., Meena, N. L. & Dahuja, A. Contemporary understanding of miRNA-based regulation of
secondary metabolites biosynthesis in plants. Front. Plant Sci. 8, 374. https://doi.org/10.3389/fpls.2017.00374 (2017).
31. Barozai, M. Y. K. Identification of microRNAs and their targets in Artemisia annua L. Pak. J. Bot. 45, 461–465 (2013).
32. Pani, A., Mahapatra, R. K., Behera, N. & Naik, P. K. J. G. Proteomics. Computational identification of sweet wormwood (Arte-
misia annua) microRNA and their mRNA targets. Genomics Proteomics Bioinform. 9, 200–210. https://doi.org/10.1016/S1672
-0229(11)60023-5 (2011).
33. Perez-Quintero, A. L. et al. Mining of miRNAs and potential targets from gene oriented clusters of transcripts sequences of the
anti-malarial plant, Artemisia annua. Biotechnol. Lett. 34, 737–745. https://doi.org/10.1007/s10529-011-0808-0 (2012).
34. Vadde, B. V. L., Challa, K. R. & Nath, U. The TCP 4 transcription factor regulates trichome cell differentiation by directly activating
GLABROUS INFLORESCENCE STEMS in Arabidopsis thaliana. Plant J. 93, 259–269. https://doi.org/10.1111/tpj.13772 (2018).
35. Judd, R. et al. Artemisinin biosynthesis in non-glandular trichome cells of Artemisia annua. Mol. Plant 12, 704–714. https://doi.
org/10.1016/j.molp.2019.02.011 (2019).
36. Olsson, M. E. et al. Localization of enzymes of artemisinin biosynthesis to the apical cells of glandular secretory trichomes of
Artemisia annua L. Phytochemistry 70, 1123–1128. https://doi.org/10.1016/j.phytochem.2009.07.009 (2009).
37. 37X., G. et al. miR828 and miR858 regulate homoeologous MYB2 gene functions in Arabidopsis trichome and cotton fibre devel-
opment. Nat. Commun. 5, 3050, https://doi.org/10.1038/ncomms4050 (2014).
38. Li, Y. et al. MicroRNA858-mediated regulation of anthocyanin biosynthesis in kiwifruit (Actinidia arguta) based on small RNA
sequencing. PLoS ONE 14, e0217480. https://doi.org/10.1371/journal.pone.0217480 (2019).
39. Fahlgren, N. & Carrington, J. C. Plant MicroRNAs 51–57 (Springer, New York, 2010).
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 10
Vol:.(1234567890)www.nature.com/scientificreports/
40. Shen, Q. et al. Overexpression of the cytochrome P450 monooxygenase (cyp71av1) and cytochrome P450 reductase (cpr) genes
increased artemisinin content in Artemisia annua (Asteraceae). Genet. Mol. Res. 11, 3298–3309. https://doi.org/10.4238/2012
(2012).
41. Xiang, L. et al. Enhancement of artemisinin biosynthesis by overexpressing dxr, cyp71av1 and cpr in the plants of Artemisia annua
L. Plant Omics 5, 503 (2012).
42. Shen, Q. et al. The jasmonate-responsive AaMYC2 transcription factor positively regulates artemisinin biosynthesis in Artemisia
annua. New Phytol. 210, 1269–1281. https://doi.org/10.1111/nph.13874 (2016).
43. Lv, Z. et al. Overexpression of a novel NAC domain-containing transcription factor gene (AaNAC1) enhances the content of arte-
misinin and increases tolerance to drought and Botrytis cinerea in Artemisia annua. Plant Cell Physiology 57, 1961–1971. https://
doi.org/10.1093/pcp/pcw118 (2016).
44. He, L. et al. Uncovering anthocyanin biosynthesis related microRNAs and their target genes by small RNA and degradome sequenc-
ing in tuberous roots of sweetpotato. BMC Plant Biol. 19, 232. https://doi.org/10.1186/s12870-019-1790-2 (2019).
45. Tirumalai, V., Swetha, C., Nair, A., Pandit, A. & Shivaprasad, P. miR828 and miR858 regulate VvMYB114 to promote anthocyanin
and flavonol accumulation in grapes. J. Exp. Bot. https://doi.org/10.1093/jxb/erz264 (2019).
46. 46Camargo-Ramirez, R., Val-Torregrosa, B., & San Segundo, B. MiR858-mediated regulation of flavonoid-specific MYB transcrip-
tion factor genes controls resistance to pathogen infection in Arabidopsis. Plant Cell Physiol. 59, 190–204, https://doi.org/10.1093/
pcp/pcx175 (2018).
47. Kawashima, C. G. et al. Sulphur starvation induces the expression of microRNA-395 and one of its target genes but in different
cell types. Plant J. 57, 313–321. https://doi.org/10.1111/j.1365-313X.2008.03690.x (2009).
48. Nunez, Y. O. et al. Positively correlated miRNA-mRNA regulatory networks in mouse frontal cortex during early stages of alcohol
dependence. BMC genomics 14, 725. https://doi.org/10.1186/1471-2164-14-725 (2013).
49. Ma, D. et al. Isolation and characterization of AaWRKY1, an Artemisia annua transcription factor that regulates the amorpha-4,
11-diene synthase gene, a key gene of artemisinin biosynthesis. Plant cell physiology 50, 2146–2161. https://doi.org/10.1093/pcp/
pcp149 (2009).
50. Han, J., Wang, H., Lundgren, A. & Brodelius, P. E. Effects of overexpression of AaWRKY1 on artemisinin biosynthesis in transgenic
Artemisia annua plants. Phytochemistry 102, 89–96. https://doi.org/10.1016/j.phytochem.2014.02.011 (2014).
51. Yang, G. et al. MicroRNAs transcriptionally regulate promoter activity in Arabidopsis thaliana. J. Integr. Plant Biol. https://doi.
org/10.1111/jipb.12775(2019).
52. Place, R. F., Li, L.-C., Pookot, D., Noonan, E. J. & Dahiya, R. MicroRNA-373 induces expression of genes with complementary
promoter sequences. Proc. Natl. Acad. Sci. 105, 1608–1613. https://doi.org/10.1073/pnas.0707594105 (2008).
53. Ro, D.-K. et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nat. Genet. 440, 940. https://
doi.org/10.1038/nature04640 (2006).
54. Ali, A. et al. RNAi-mediated modulation of squalene synthase gene expression in Artemisia annua L. and its impact on artemisinin
biosynthesis. Rendiconti Lincei 28, 731–741. https://doi.org/10.1007/s12210-017-0647-6 (2017).
55. 55Khanuja, S. P. S. et al. (Google Patents, 2008).
56. Dai, X., Zhuang, Z. & Zhao, P. X. psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Res. 46,
W49–W54. https://doi.org/10.1093/nar/gky316 (2018).
57. Varkonyi-Gasic, E. Plant Epigenetics 163–175 (Springer, New York, 2017).
58. 58Akoglu, H. J. T. J. O. E. M. User’s guide to correlation coefficients. 18, 91–93 (2018).
59. 59Motulsky, H. J. G. S. Prism 5 statistics guide, 2007. 31, 39–42 (2007).
Acknowledgements
The research facilities developed from UGC-SAP (DRS-I and DRSII) grant sanctioned to the Department of
Biotechnology and the senior research fellowship to SK under UGC-BSR scheme by University Grants Com-
mission, Government of India are gratefully acknowledged.
Author contributions
S.K., A.A., and M.Z. conceived the study and designed the experiments. S.K., A.A., M.S., S.A., and P.S. conducted
the experiments. S.K. wrote the manuscript. M.Z. interpreted and discussed the results and edited the manuscript.
All authors reviewed the manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41598-020-69707-3.
Correspondence and requests for materials should be addressed to M.Z.A.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© The Author(s) 2020
Scientific Reports | (2020) 10:13614 | https://doi.org/10.1038/s41598-020-69707-3 11
Vol.:(0123456789)You can also read
